Presubmission Inquiry: GLMMcosinor

[RWParsons]

Submitting Author Name: Rex Parsons Submitting Author Github Handle: !--author1-->@RWParsons<!--end-author1-- Other Package Authors Github handles: (comma separated, delete if none) @oliverjayasinghe, @nicolemwhite Repository: https://github.com/RWParsons/GLMMcosinor Submission type: Pre-submission Language: en

---

• Paste the full DESCRIPTION file inside a code block below:

Type: Package
Package: GLMMcosinor
Title: Fit a cosinor model using a generalised mixed modelling framework
Version: 0.0.9000
Authors@R: c(
    person("Rex", "Parsons", , "rex.parsons94@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-6053-8174")),
    person("Oliver", "Jayasinghe", role = "aut"),
    person("Nicole", "White", role = "aut",
           comment = c(ORCID = "0000-0002-9292-0773"))
  )
Description: Fit the cosinor model using the glmmTMB framework, allowing
    for a wide range of link functions and capacity of heirachical
    structures.
License: GPL (>= 3)
URL: https://github.com/RWParsons/GLMMcosinor,
    https://rwparsons.github.io/GLMMcosinor/
BugReports: https://github.com/RWParsons/GLMMcosinor/issues
Imports:
    assertthat,
    cowplot,
    ggforce,
    ggplot2,
    glmmTMB,
    lme4,
    rlang,
    scales,
    stats
Suggests:
    covr,
    dplyr,
    DT,
    flextable,
    ftExtra,
    knitr,
    rmarkdown,
    testthat (>= 3.0.0),
    vdiffr,
    withr
VignetteBuilder: 
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
Roxygen: list(markdown = TRUE, roclets = c ("namespace", "rd",
    "srr::srr_stats_roclet"))
RoxygenNote: 7.2.3

Scope

• Please indicate which category or categories from our package fit policies or statistical package categories this package falls under. (Please check an appropriate box below):

  Data Lifecycle Packages

  • [ ] data retrieval

  • [ ] data extraction

  • [ ] data munging

  • [ ] data deposition

    • [ ] data validation and testing

  • [ ] workflow automation

  • [ ] version control

  • [ ] citation management and bibliometrics

  • [ ] scientific software wrappers

  • [ ] field and lab reproducibility tools

  • [ ] database software bindings

  • [ ] geospatial data

  • [ ] text analysis

    Statistical Packages

  • [ ] Bayesian and Monte Carlo Routines

  • [ ] Dimensionality Reduction, Clustering, and Unsupervised Learning

  • [ ] Machine Learning

  • [x] Regression and Supervised Learning

  • [ ] Exploratory Data Analysis (EDA) and Summary Statistics

  • [ ] Spatial Analyses

  • [ ] Time Series Analyses

• Explain how and why the package falls under these categories (briefly, 1-2 sentences). Please note any areas you are unsure of:

GLMMcosinor is a package that makes fitting (cosinor) regression models easy and via the glmmTMB framework.

• If submitting a statistical package, have you already incorporated documentation of standards into your code via the srr package?

Yes, the standards are included in the relevant Roxygen skeletons.

• Who is the target audience and what are scientific applications of this package?

People analysing rhythmic/circular data - for example, circadian biologists.

• Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?

While there are a few existing R packages that help the user fit a cosinor or nonlinear model to circular data, none in R (but two in python but are limited to count data-related link functions) use a generalised framework, only one ({circacompare}, but fits a nonlinear model, not a cosinor model) allows the specification of a mixed model, very few allow the user to specify other covariates in the model or interaction terms on the cosinor components, and none include all of these features together. GLMMcosinor achieves this by using glmmTMB under the hood rather than lm() (as, for example, {cosinor} does). We have also developed functions to visualise the cosinor components in either polar or time series plots.

The README of the repository shows a table comparing available softare.

• (If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?

NA

• Any other questions or issues we should be aware of?:

NA

[noamross]

Thank you for your presubmission @RWParsons! Yes, this package makes sense as a submission under the Statistical Regression and Supervised Learning category. We welcome a full submissiom.